Tuberculosis (TB) is a global health problem of major concern. Identification of immune biomarkers may facilitate the early diagnosis and targeted treatment of TB. We used public RNA-sequencing datasets of patients with TB and healthy controls to identify differentially expressed genes and their associated functional networks. GBP1 expression was consistently significantly upregulated in TB, and 4492 differentially expressed genes were simultaneously associated with TB and high GBP1 expression. Weighted gene correlation analysis identified 12 functional modules. Modules positively correlated with TB and high GBP1 expression were associated with the innate immune response, neutrophil activation, neutrophil-mediated immunity, and NOD receptor signaling pathway. Eleven hub genes (GBP1, HLA-B, ELF4, HLA-E, IFITM2, TNFRSF14, CD274, AIM2, CFB, RHOG, and HORMAD1) were identified. The least absolute shrinkage and selection operator model based on hub genes accurately predicted the occurrence of TB (area under the receiver operating characteristic curve = 0.97). The GBP1-module-pathway network based on the STRING database showed that GBP1 expression correlated with the expression of interferon-stimulated genes (GBP5, BATF2, EPSTI1, RSAD2, IFI44L, IFIT3, and OAS3). Our study suggests GBP1 as an optimal diagnostic biomarker for TB, further indicating an association of the AIM2 inflammasome signaling pathway in TB pathology.
© 2022. The Author(s).